Existing approaches to vehicle detection often include the use of active sensors, such as lasers, lidar, or millimeter-wave radars. These detect the distance of an object by measuring the travel time of a signal emitted by the sensors and reflected by the object. Such sensors have a variety of drawbacks, such as low spatial resolution and slow scanning speed. Additionally, when a large number of vehicles are moving simultaneously in the same direction, interference among sensors of the same type may pose a big problem.
Passive sensors, such as for example optical sensors in cameras, are also known approaches to vehicle detection. These types of sensors are lower cost and acquire data in a less intrusive manner than active sensors. However, there are large variables in the use of optical sensors that make vehicle detection challenging and problematic. For example, vehicles vary widely in color, size and shape, and their appearance to a camera is often inconsistent or unclear, resulting in unreliable accuracy. Also, the presence of other objects, changes in vehicle speed and location, environmental conditions such as changes in illumination, unpredictable interaction with other vehicles and behavior of other vehicles, and other background noise add to difficulty in arriving at accurate and reliable results.
Regardless of the type of sensor, another issue with existing approaches involves time needed to processes images sensed. Most approaches involve capturing large images of entire vehicles, yet driver assistance applications and vehicle detection algorithms need to process images very quickly, in real-time or as close to real-time as possible. Searching entire images to determine vehicle locations and process a potential warning the driver with enough time to react is often not possible or realistic.